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Title: Doctor for AI: Prior-informed ML for Biomedical Imaging and Perception
Abstract: Deepening our understanding of human health is more important than ever before to address real-world challenges in biomedicine and healthcare, especially with the pandemic over recent years. My research focuses on AI in medicine, to develop efficient ML models for biomedical imaging and perception for addressing real-world challenges. In this talk, I will first explore the challenges in this emerging field and then present the two following lines of my work:
First, I will introduce my work in Doctor for AI: leverage the prior knowledge of doctors to design AI model for biomedical imaging. Specifically, I will discuss how to integrate different kinds of prior knowledge to develop reliable data-efficient ML models, by exploiting the personalized prior, population prior and physics prior. With the innovative ML models, the proposed approaches can be generally applied to various biomedical imaging applications including sparse-sampling image reconstruction, projection synthesis, and molecular imaging.
Second, I will introduce my work in AI for Doctor: develop ML-driven perception models that can adaptive to unique characteristics of biomedical data including random noise and multi-modality. Specifically, I will present a self-attention-guided ML model for quantitative image perception. Through international collaborations for cross-institute validation among four U.S. clinical centers and a Turkey institute, this work demonstrates the possibility for the developed ML model to characterize the in utero neurodevelopmental trajectory in real-world deployment.